Extended correlated principal component analysis with SVM-PUK in opinion mining

Abstract

With the rapid growth of microblogs and online sites, an inordinate number of product reviews are availableon the Internet. They not only help in analyzing, but also assist in making informed decisions about product quality. Inthe proposed work, an extended correlated principal component analysis (ECPCA) is used for dimensionality reduction.A comparative analysis is conducted on movie reviews (DB-1) and Twitter datasets (DB-2 and DB-3) in opinion miningextraction. The performance of naïve Bayes, CHIRP, and support vector machine (SVM) with kernel methods such asradial basis function (RBF), polynomial, and Pearson (PUK) are compared and analyzed on the three datasets. Theexperimental results using ECPCA for selecting relevant features and SVM-PUK as a classifier exhibit better performanceon movie reviews and Twitter datasets. The performance of the proposed approach is 99.69%, 99.4%, and 99.54% onthe DB-1, DB-2, and DB-3 datasets, respectively, and comparatively outperforms the existing methods.